Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App


In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.


Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Write your Algorithm
  • Step 6: Test Your Algorithm

Step 0: Import Datasets

Make sure that you've downloaded the required human and dog datasets:

Note: if you are using the Udacity workspace, you DO NOT need to re-download these - they can be found in the /data folder as noted in the cell below.

  • Download the dog dataset. Unzip the folder and place it in this project's home directory, at the location /dog_images.

  • Download the human dataset. Unzip the folder and place it in the home directory, at location /lfw.

Note: If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.

In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays human_files and dog_files.

In [1]:
import numpy as np
from glob import glob

# load filenames for human and dog images
human_files = np.array(glob("/data/lfw/*/*"))
dog_files = np.array(glob("/data/dog_images/*/*/*"))

# print number of images in each dataset
print('There are %d total human images.' % len(human_files))
print('There are %d total dog images.' % len(dog_files))
There are 13233 total human images.
There are 8351 total dog images.

Step 1: Detect Humans

In this section, we use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images.

OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory. In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [2]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[1001])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [3]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer: humans detected = 98 % dogs detected = 17% (You can print out your results and/or write your percentages in this cell)

In [4]:
human_files_short = human_files[:100]
dog_files_short = dog_files[:100]
In [4]:
from tqdm import tqdm



#-#-# Do NOT modify the code above this line. #-#-#

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
human = 0.0
dog = 0.0
for img in human_files_short:
    if face_detector(img):
        human = human + 1

for img in dog_files_short:
    if  face_detector(img):
        dog = dog + 1
dog  /=  len(dog_files_short)
dog *= 100
In [5]:
print('human = {human}'.format(human = human))
print('dog = {dog}'.format(dog = dog))
human = 98.0
dog = 17.0

We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.

In [6]:
### (Optional) 
### TODO: Test performance of anotherface detection algorithm.
### Feel free to use as many code cells as needed.

Step 2: Detect Dogs

In this section, we use a pre-trained model to detect dogs in images.

Obtain Pre-trained VGG-16 Model

The code cell below downloads the VGG-16 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories.

In [5]:
import torch
import torchvision.models as models

# define VGG16 model
VGG16 = models.vgg16(pretrained=True)

# check if CUDA is available
use_cuda = torch.cuda.is_available()

# move model to GPU if CUDA is available
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.torch/models/vgg16-397923af.pth
100%|██████████| 553433881/553433881 [00:05<00:00, 104331962.54it/s]

Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image.

In [6]:
print(use_cuda)
True

(IMPLEMENTATION) Making Predictions with a Pre-trained Model

In the next code cell, you will write a function that accepts a path to an image (such as 'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg') as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model. The output should always be an integer between 0 and 999, inclusive.

Before writing the function, make sure that you take the time to learn how to appropriately pre-process tensors for pre-trained models in the PyTorch documentation.

In [7]:
from PIL import Image
import torchvision.transforms as transforms
def VGG16_predict(img_path):
    '''
    Use pre-trained VGG-16 model to obtain index corresponding to 
    predicted ImageNet class for image at specified path
    
    Args:
        img_path: path to an image
        
    Returns:
        Index corresponding to VGG-16 model's prediction
    '''
    ## TODO: Complete the function.
    ## Load and pre-process an image from the given img_path
    ## Return the *index* of the predicted class for that image
    img = Image.open(img_path)
    transform = transforms.Compose([transforms.RandomResizedCrop(224),
#                                 transforms.RandomRotation(10),
                                transforms.ToTensor(),
                                transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                                     std=[0.229, 0.224, 0.225])])
    img = transform(img).float()
    img = torch.tensor(img , requires_grad = True)
    img = img.unsqueeze(0)
    if use_cuda:
        VGG16.cuda()
        img = img.cuda()
    output = VGG16(img)
    _ , indx = torch.topk(output , 1, dim=1)
    return indx.item() # predicted class index

(IMPLEMENTATION) Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).

Use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [8]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    ## TODO: Complete the function.
    indx = VGG16_predict(img_path)
    return (151 <= indx <= 268)

(IMPLEMENTATION) Assess the Dog Detector

Question 2: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer: dogs detected in human_files_short = 0% dogs detected in dog_files_short = 96%

In [9]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
human = 0
dog = 0
human_files_short = human_files[:100]
dog_files_short = dog_files[:100]
for imgs in human_files_short:
    if dog_detector(imgs):
        human += 1
for imgs in dog_files_short:
    if dog_detector(imgs):
        dog += 1

print('human = {} \n dog = {}'.format(human , dog))
human = 0 
 dog = 98

We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as Inception-v3, ResNet-50, etc). Please use the code cell below to test other pre-trained PyTorch models. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.

In [10]:
### (Optional) 
### TODO: Report the performance of another pre-trained network.
### Feel free to use as many code cells as needed.

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 10%. In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset

Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dog_images/train, dog_images/valid, and dog_images/test, respectively). You may find this documentation on custom datasets to be a useful resource. If you are interested in augmenting your training and/or validation data, check out the wide variety of transforms!

Question 3: Describe your chosen procedure for preprocessing the data.

  • How does your code resize the images (by cropping, stretching, etc)? What size did you pick for the input tensor, and why?
  • Did you decide to augment the dataset? If so, how (through translations, flips, rotations, etc)? If not, why not?
In [11]:
import os,torch
from torchvision import datasets,transforms

### TODO: Write data loaders for training, validation, and test sets
## Specify appropriate transforms, and batch_sizes
transform = transforms.Compose([transforms.RandomResizedCrop(224),
                                transforms.RandomRotation(10),
                                transforms.ToTensor(),
                                transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                                     std=[0.229, 0.224, 0.225])])

transform_else = transforms.Compose([transforms.RandomResizedCrop(224),
                                    transforms.ToTensor(),
                                    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                                     std=[0.229, 0.224, 0.225])])

loaders_scratch = {}
dir = '/data/dog_images/'
train_data = datasets.ImageFolder(dir + 'train' , transform = transform)
test_data = datasets.ImageFolder(dir+'test',transform = transform_else)
validation_data = datasets.ImageFolder(dir+'valid',transform = transform_else)
loaders_scratch['train'] = torch.utils.data.DataLoader(train_data , batch_size = 32 , shuffle = True )
loaders_scratch['test'] = torch.utils.data.DataLoader(test_data , batch_size = 32 , shuffle = True )
loaders_scratch['valid'] = torch.utils.data.DataLoader(validation_data , batch_size = 32 , shuffle = True )

Answer: 1. I resized the images using transforms.RandomResizedCrop(224) . The input tensor is of size 224 x 224 as the VGG16 model uses the same , so its better to start from pretrained model data.

  1. I augmented the input by rotating the input 10 degrees randomly

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. Use the template in the code cell below.

In [12]:
import torch.nn as nn
import torch.nn.functional as F

# define the CNN architecture
class Net(nn.Module):
    ### TODO: choose an architecture, and complete the class
    def __init__(self):
        super(Net, self).__init__()
        ## Define layers of a CNN
        self.conv1 = nn.Conv2d(3, 16, 3, stride = 1,padding = 1)
        self.conv2 = nn.Conv2d(16 , 32, 3 , stride=1 , padding = 1)
        self.conv3 = nn.Conv2d(32, 64, 3, stride=1,padding = 1)
        self.conv4 = nn.Conv2d(64, 128, 3, stride=1,padding = 1)
        self.pool = nn.MaxPool2d(2,2)
        self.fc1 = nn.Linear(128*14*14,1024)
        self.fc2 = nn.Linear(1024,133)
        self.dropout = nn.Dropout(p=0.2)
    
    def forward(self, x):
        ## Define forward behavior
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.pool(x)
        x = F.relu(self.conv3(x))
        x = self.pool(x)
        x = F.relu(self.conv4(x))
        x = self.pool(x)
        x = self.dropout(x)
        x = x.view(-1,128*14*14)
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

#-#-# You so NOT have to modify the code below this line. #-#-#

# instantiate the CNN
model_scratch = Net()

# move tensors to GPU if CUDA is available
use_cuda = torch.cuda.is_available()
if use_cuda:
    model_scratch.cuda()
In [13]:
model_scratch
Out[13]:
Net(
  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (fc1): Linear(in_features=25088, out_features=1024, bias=True)
  (fc2): Linear(in_features=1024, out_features=133, bias=True)
  (dropout): Dropout(p=0.2)
)

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step.

Answer: 4 convolutional layers upto a depth of 128 with max pooling layers after each convolutional layer . And finally 2 fully connected layers with the output layer having 133 out_features for 133 dog breeds .

(IMPLEMENTATION) Specify Loss Function and Optimizer

Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_scratch, and the optimizer as optimizer_scratch below.

In [14]:
import torch.optim as optim

### TODO: select loss function
criterion_scratch = nn.CrossEntropyLoss()

### TODO: select optimizer
optimizer_scratch = optim.SGD(model_scratch.parameters() , lr=0.005)

(IMPLEMENTATION) Train and Validate the Model

Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_scratch.pt'.

In [34]:
import numpy as np
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):
    """returns trained model"""
    # initialize tracker for minimum validation loss
    valid_loss_min = np.Inf 
    if use_cuda:
        model=model.cuda()
    for epoch in range(1, n_epochs+1):
        # initialize variables to monitor training and validation loss
        train_loss = 0.0
        valid_loss = 0.0
        
        ###################
        # train the model #
        ###################
        model.train()
        for batch_idx, (data, target) in enumerate(loaders['train']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            ## find the loss and update the model parameters accordingly
            optimizer.zero_grad()
            out = model(data)
            loss = criterion(out,target)
            loss.backward()
            optimizer.step()
            #train_loss += loss.item()
            ## record the average training loss, using something like
            train_loss +=  ((1 / (batch_idx + 1)) * (loss.data - train_loss))
            ## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))
        #train_loss /= len(loaders['train'])
        ######################    
        # validate the model #
        ######################
        model.eval()
        for batch_idx, (data, target) in enumerate(loaders['valid']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            ## update the average validation loss
            out = model(data)
            vloss = criterion(out,target)
            valid_loss +=  ((1 / (batch_idx + 1)) * (loss.data - valid_loss))
            #valid_loss += vloss.item()
        
        # print training/validation statistics 
        print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
            epoch, 
            train_loss,
            valid_loss
            ))
        
        ## TODO: save the model if validation loss has decreased
        if( valid_loss < valid_loss_min ):
            valid_loss_min = valid_loss
            torch.save(model.state_dict() , save_path)
    # return trained model
    return model


# train the model
model_scratch = train(15, loaders_scratch, model_scratch, optimizer_scratch, 
                      criterion_scratch, use_cuda, 'model_scratch.pt')
Epoch: 1 	Training Loss: 3.184692 	Validation Loss: 2.476899
Epoch: 2 	Training Loss: 3.140727 	Validation Loss: 3.601475
Epoch: 3 	Training Loss: 3.115508 	Validation Loss: 2.862750
Epoch: 4 	Training Loss: 3.092002 	Validation Loss: 3.180226
Epoch: 5 	Training Loss: 3.081253 	Validation Loss: 3.499930
Epoch: 6 	Training Loss: 3.017542 	Validation Loss: 2.713533
Epoch: 7 	Training Loss: 3.026326 	Validation Loss: 3.050671
Epoch: 8 	Training Loss: 2.987472 	Validation Loss: 3.178815
Epoch: 9 	Training Loss: 2.973701 	Validation Loss: 2.826012
Epoch: 10 	Training Loss: 2.953531 	Validation Loss: 2.675200
Epoch: 11 	Training Loss: 2.905152 	Validation Loss: 2.805898
Epoch: 12 	Training Loss: 2.898278 	Validation Loss: 3.061414
Epoch: 13 	Training Loss: 2.850674 	Validation Loss: 2.598255
Epoch: 14 	Training Loss: 2.854186 	Validation Loss: 3.017030
Epoch: 15 	Training Loss: 2.830748 	Validation Loss: 3.385027
In [35]:
# load the model that got the best validation accuracy
model_scratch.load_state_dict(torch.load('model_scratch.pt'))

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 10%.

In [36]:
def test(loaders, model, criterion, use_cuda):

    # monitor test loss and accuracy
    test_loss = 0.
    correct = 0.
    total = 0.

    model.eval()
    for batch_idx, (data, target) in enumerate(loaders['test']):
        # move to GPU
        if use_cuda:
            data, target = data.cuda(), target.cuda()
        # forward pass: compute predicted outputs by passing inputs to the model
        output = model(data)
        # calculate the loss
        loss = criterion(output, target)
        # update average test loss 
        test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
        # convert output probabilities to predicted class
        pred = output.data.max(1, keepdim=True)[1]
        # compare predictions to true label
        correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
        total += data.size(0)
            
    print('Test Loss: {:.6f}\n'.format(test_loss))

    print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
        100. * correct / total, correct, total))

# call test function    
test(loaders_scratch, model_scratch, criterion_scratch, use_cuda)
Test Loss: 4.022920


Test Accuracy: 12% (108/836)

Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset

Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dogImages/train, dogImages/valid, and dogImages/test, respectively).

If you like, you are welcome to use the same data loaders from the previous step, when you created a CNN from scratch.

In [18]:
## TODO: Specify data loaders
import torch
from torchvision import transforms,datasets
loaders_transfer = {}
dir = '/data/dog_images/'
train_data = datasets.ImageFolder(dir + 'train' , transform = transform)
test_data = datasets.ImageFolder(dir+'test',transform = transform_else)
validation_data = datasets.ImageFolder(dir+'valid',transform = transform_else)
data = { "train": train_data , "test": test_data , "valid" : validation_data }
loaders_transfer['train'] = torch.utils.data.DataLoader(train_data , batch_size = 32 , shuffle = True )
loaders_transfer['test'] = torch.utils.data.DataLoader(test_data , batch_size = 32 , shuffle = True )
loaders_transfer['valid'] = torch.utils.data.DataLoader(validation_data , batch_size = 32 , shuffle = True )

(IMPLEMENTATION) Model Architecture

Use transfer learning to create a CNN to classify dog breed. Use the code cell below, and save your initialized model as the variable model_transfer.

In [19]:
import torchvision.models as models
import torch.nn as nn
use_cuda = torch.cuda.is_available()
## TODO: Specify model architecture 
model_transfer = models.vgg19(pretrained=True)

if use_cuda:
    model_transfer = model_transfer.cuda()
Downloading: "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth" to /root/.torch/models/vgg19-dcbb9e9d.pth
100%|██████████| 574673361/574673361 [00:05<00:00, 102156302.87it/s]

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer: used the pretrained VGG19 model with all the convultional layers as pretrained , finally replaced the last fully connected layer with 133 out_features for all the dog breed classes. VGG19 is already pretrained with a huge dataset covering 1000 classes and using this pretrained model with a little bit of fine tuning shall do my work

(IMPLEMENTATION) Specify Loss Function and Optimizer

Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_transfer, and the optimizer as optimizer_transfer below.

In [20]:
from torch import optim
criterion_transfer = nn.CrossEntropyLoss()
optimizer_transfer = optim.SGD(model_transfer.parameters() , lr=0.005)
In [21]:
model_transfer
Out[21]:
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace)
    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (17): ReLU(inplace)
    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace)
    (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (24): ReLU(inplace)
    (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (26): ReLU(inplace)
    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace)
    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (31): ReLU(inplace)
    (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (33): ReLU(inplace)
    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (35): ReLU(inplace)
    (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)

(IMPLEMENTATION) Train and Validate the Model

Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_transfer.pt'.

In [22]:
model_transfer.classifier[6] = nn.Linear(4096,133)
model_transfer
Out[22]:
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace)
    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (17): ReLU(inplace)
    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace)
    (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (24): ReLU(inplace)
    (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (26): ReLU(inplace)
    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace)
    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (31): ReLU(inplace)
    (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (33): ReLU(inplace)
    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (35): ReLU(inplace)
    (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): Linear(in_features=4096, out_features=133, bias=True)
  )
)
In [23]:
for layers in model_transfer.features:
    layers.requires_grad = False
#model_transfer.fc.requires_grad = True
use_cuda
Out[23]:
True
In [24]:
import numpy as np
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):
    """returns trained model"""
    # initialize tracker for minimum validation loss
    valid_loss_min = np.Inf 
    if use_cuda:
        model=model.cuda()
    for epoch in range(1, n_epochs+1):
        # initialize variables to monitor training and validation loss
        train_loss = 0.0
        valid_loss = 0.0
        
        ###################
        # train the model #
        ###################
        model.train()
        for batch_idx, (data, target) in enumerate(loaders['train']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            ## find the loss and update the model parameters accordingly
            optimizer.zero_grad()
            out = model(data)
            loss = criterion(out,target)
            loss.backward()
            optimizer.step()
            #train_loss += loss.item()
            ## record the average training loss, using something like
            train_loss +=  ((1 / (batch_idx + 1)) * (loss.data - train_loss))
            ## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))
        #train_loss /= len(loaders['train'])
        ######################    
        # validate the model #
        ######################
        model.eval()
        for batch_idx, (data, target) in enumerate(loaders['valid']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            ## update the average validation loss
            out = model(data)
            vloss = criterion(out,target)
            valid_loss +=  ((1 / (batch_idx + 1)) * (loss.data - valid_loss))
            #valid_loss += vloss.item()
        
        # print training/validation statistics 
        print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
            epoch, 
            train_loss,
            valid_loss
            ))
        
        ## TODO: save the model if validation loss has decreased
        if( valid_loss < valid_loss_min ):
            valid_loss_min = valid_loss
            torch.save(model.state_dict() , save_path)
    # return trained model
    return model
model_transfer.load_state_dict(torch.load('model_transfer.pt'))
In [34]:
# train the model

model_transfer = train(10, loaders_transfer, model_transfer, optimizer_transfer, criterion_transfer, use_cuda, 'model_transfer.pt')


# load the model that got the best validation accuracy (uncomment the line below)
model_transfer.load_state_dict(torch.load('model_transfer.pt'))
Epoch: 1 	Training Loss: 1.299796 	Validation Loss: 1.206658
Epoch: 2 	Training Loss: 1.214888 	Validation Loss: 1.038810
Epoch: 3 	Training Loss: 1.178983 	Validation Loss: 1.459292
Epoch: 4 	Training Loss: 1.138359 	Validation Loss: 1.703570
Epoch: 5 	Training Loss: 1.052682 	Validation Loss: 1.011091
Epoch: 6 	Training Loss: 1.026725 	Validation Loss: 0.743528
Epoch: 7 	Training Loss: 0.980927 	Validation Loss: 0.951183
Epoch: 8 	Training Loss: 0.950280 	Validation Loss: 0.669336
Epoch: 9 	Training Loss: 0.922208 	Validation Loss: 0.644818
Epoch: 10 	Training Loss: 0.945851 	Validation Loss: 1.095421

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 60%.

In [25]:
if use_cuda:
    model_transfer = model_transfer.cuda()
test(loaders_transfer, model_transfer, criterion_transfer, use_cuda)
Test Loss: 0.951834


Test Accuracy: 74% (622/836)
In [26]:
def imgdisplay(img_path):
    img = cv2.imread(img_path)
    # convert BGR image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # find faces in image
    faces = face_cascade.detectMultiScale(gray)

    # print number of faces detected in the image
    print('Number of faces detected:', len(faces))

    # get bounding box for each detected face
    # convert BGR image to RGB for plotting
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # display the image, along with bounding box
    plt.imshow(cv_rgb)
    plt.show()

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan hound, etc) that is predicted by your model.

In [27]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.

# list of class names by index, i.e. a name can be accessed like class_names[0]
class_names = [item[4:].replace("_", " ") for item in data['train'].classes]

def predict_breed_transfer(img_path):
    # load the image and return the predicted breed
    model_transfer.cuda()
    img = Image.open(img_path)
    transform = transforms.Compose([transforms.RandomResizedCrop(224),
                                    transforms.ToTensor(),
                                    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                                     std=[0.229, 0.224, 0.225])])
    img = transform(img).float()
    img = img.unsqueeze(0)
    img=img.cuda()
    out = model_transfer(img)
    _ , indx = torch.topk(out , 1 , dim=1)
    indx = indx.item()
    return class_names[indx]

Step 5: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and human_detector functions developed above. You are required to use your CNN from Step 4 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm

In [31]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.

def run_app(img_path):
    ## handle cases for a human face, dog, and neither
    dog = dog_detector(img_path)
    if dog:
        class1 = predict_breed_transfer(img_path)
        print('hey dog .. ')
        imgdisplay(img_path)
        print('you are a {}'.format(class1))
    elif face_detector(img_path):
        print('hello human!')
        class1 = predict_breed_transfer(img_path)
        imgdisplay(img_path)
        print('You look like a... \n {}'.format(class1))       
    else:
        imgdisplay(img_path)
        print('you arent any human or a dog.. i am sorry i couldnt classify you... :-( ')

Step 6: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer: 1. Training the fully connected layers for longer might decrease the validation loss to a further extent

  1. Pictures in which both humans and dogs are present gives a biased prediction
  2. Other pretrained models might give me better test accuracy
In [33]:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.

## suggested code, below
#model_transfer = model_transfer.to(device)
testhuman = np.random.randint(0 , len(human_files), size = 50)
testdog = np.random.randint(0 , len(dog_files) , size=50)
for file in np.hstack((human_files[testhuman], dog_files[testdog])):
    run_app(file)
hello human!
Number of faces detected: 1
You look like a... 
 Nova scotia duck tolling retriever
hello human!
Number of faces detected: 1
You look like a... 
 Dandie dinmont terrier
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Chesapeake bay retriever
hello human!
Number of faces detected: 2
You look like a... 
 Nova scotia duck tolling retriever
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Irish water spaniel
hey dog .. 
Number of faces detected: 1
you are a Nova scotia duck tolling retriever
hello human!
Number of faces detected: 1
You look like a... 
 Chesapeake bay retriever
hello human!
Number of faces detected: 1
You look like a... 
 Flat-coated retriever
hello human!
Number of faces detected: 1
You look like a... 
 Pharaoh hound
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Nova scotia duck tolling retriever
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Nova scotia duck tolling retriever
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Pharaoh hound
hello human!
Number of faces detected: 1
You look like a... 
 Chinese crested
hello human!
Number of faces detected: 2
You look like a... 
 Basset hound
hello human!
Number of faces detected: 1
You look like a... 
 Nova scotia duck tolling retriever
hello human!
Number of faces detected: 1
You look like a... 
 English toy spaniel
hello human!
Number of faces detected: 1
You look like a... 
 Irish water spaniel
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Great dane
hello human!
Number of faces detected: 1
You look like a... 
 Manchester terrier
hello human!
Number of faces detected: 2
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 American staffordshire terrier
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 American foxhound
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Basenji
hello human!
Number of faces detected: 1
You look like a... 
 Great dane
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Poodle
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Pharaoh hound
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Nova scotia duck tolling retriever
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Nova scotia duck tolling retriever
hello human!
Number of faces detected: 1
You look like a... 
 Dogue de bordeaux
hello human!
Number of faces detected: 1
You look like a... 
 Pharaoh hound
hello human!
Number of faces detected: 1
You look like a... 
 Manchester terrier
hey dog .. 
Number of faces detected: 0
you are a Italian greyhound
hey dog .. 
Number of faces detected: 0
you are a Bouvier des flandres
hey dog .. 
Number of faces detected: 0
you are a Chinese crested
hey dog .. 
Number of faces detected: 0
you are a Pointer
Number of faces detected: 0
you arent any human or a dog.. i am sorry i couldnt classify you... :-( 
hey dog .. 
Number of faces detected: 0
you are a Cane corso
hey dog .. 
Number of faces detected: 0
you are a Ibizan hound
hey dog .. 
Number of faces detected: 0
you are a Chinese crested
hey dog .. 
Number of faces detected: 0
you are a Bulldog
hey dog .. 
Number of faces detected: 0
you are a Afghan hound
hey dog .. 
Number of faces detected: 0
you are a French bulldog
hey dog .. 
Number of faces detected: 0
you are a Xoloitzcuintli
hey dog .. 
Number of faces detected: 0
you are a Parson russell terrier
hey dog .. 
Number of faces detected: 0
you are a Alaskan malamute
hey dog .. 
Number of faces detected: 0
you are a Brittany
hey dog .. 
Number of faces detected: 0
you are a Maltese
hey dog .. 
Number of faces detected: 0
you are a Alaskan malamute
Number of faces detected: 0
you arent any human or a dog.. i am sorry i couldnt classify you... :-( 
hey dog .. 
Number of faces detected: 0
you are a Maltese
hey dog .. 
Number of faces detected: 0
you are a American eskimo dog
hey dog .. 
Number of faces detected: 0
you are a Bull terrier
hey dog .. 
Number of faces detected: 0
you are a Japanese chin
hey dog .. 
Number of faces detected: 0
you are a Wirehaired pointing griffon
hey dog .. 
Number of faces detected: 0
you are a Australian cattle dog
hey dog .. 
Number of faces detected: 0
you are a Leonberger
Number of faces detected: 0
you arent any human or a dog.. i am sorry i couldnt classify you... :-( 
hey dog .. 
Number of faces detected: 1
you are a Keeshond
hey dog .. 
Number of faces detected: 0
you are a Briard
hey dog .. 
Number of faces detected: 0
you are a Chihuahua
hey dog .. 
Number of faces detected: 1
you are a Akita
hey dog .. 
Number of faces detected: 0
you are a Labrador retriever
hey dog .. 
Number of faces detected: 0
you are a Dachshund
hey dog .. 
Number of faces detected: 0
you are a Italian greyhound
hey dog .. 
Number of faces detected: 0
you are a Labrador retriever
hey dog .. 
Number of faces detected: 0
you are a Chinese shar-pei
hey dog .. 
Number of faces detected: 0
you are a Bulldog
hey dog .. 
Number of faces detected: 0
you are a Basset hound
hey dog .. 
Number of faces detected: 0
you are a Norfolk terrier
hey dog .. 
Number of faces detected: 0
you are a German shepherd dog
hey dog .. 
Number of faces detected: 1
you are a Great dane
hey dog .. 
Number of faces detected: 0
you are a English setter
hey dog .. 
Number of faces detected: 0
you are a Border collie
hey dog .. 
Number of faces detected: 0
you are a Bloodhound
hey dog .. 
Number of faces detected: 0
you are a Affenpinscher
Number of faces detected: 0
you arent any human or a dog.. i am sorry i couldnt classify you... :-( 
hey dog .. 
Number of faces detected: 1
you are a English springer spaniel
hey dog .. 
Number of faces detected: 0
you are a Akita
hey dog .. 
Number of faces detected: 0
you are a Greyhound
hey dog .. 
Number of faces detected: 1
you are a Cardigan welsh corgi
hey dog .. 
Number of faces detected: 0
you are a Pointer
In [ ]: